create_stream_processor ¶

def create_stream_processor(
    self,
    input: "capo_rekognition.types.stream_processor_input.StreamProcessorInput",
    output: "capo_rekognition.types.stream_processor_output.StreamProcessorOutput",
    name: "capo_rekognition.types.stream_processor_name.StreamProcessorName",
    settings: "capo_rekognition.types.stream_processor_settings.StreamProcessorSettings",
    role_arn: "capo_rekognition.types.role_arn.RoleArn",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    tags: Optional["capo_rekognition.types.tag_map.TagMap"] = None,
    notification_channel: Optional[
        "capo_rekognition.types.stream_processor_notification_channel.StreamProcessorNotificationChannel"
    ] = None,
    kms_key_id: Optional["capo_rekognition.types.kms_key_id.KmsKeyId"] = None,
    regions_of_interest: Optional[
        "capo_rekognition.types.regions_of_interest.RegionsOfInterest"
    ] = None,
    data_sharing_preference: Optional[
        "capo_rekognition.types.stream_processor_data_sharing_preference.StreamProcessorDataSharingPreference"
    ] = None,
) -> "capo_rekognition.types.create_stream_processor_response.CreateStreamProcessorResponse":
    """<important> <p>Service availability notice: Streaming Video and Bulk Image Analysis is no longer available to new customers. For more information, see <a href="https://docs.aws.amazon.com/rekognition/latest/dg/rekognition-availability-changes.html">Rekognition feature availability changes</a>.</p> <p> <b>This change does not impact the availability of other Amazon Rekognition features.</b> </p> </important> <p>Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.</p> <p>Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.</p> <ul> <li> <p>If you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (<code>Input</code>) and a Kinesis data stream (<code>Output</code>) stream for receiving the output. You must use the <code>FaceSearch</code> option in <code>Settings</code>, specifying the collection that contains the faces you want to recognize. After you have finished analyzing a streaming video, use <a>StopStreamProcessor</a> to stop processing.</p> </li> <li> <p>If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (<code>Input</code>), Amazon S3 bucket information (<code>Output</code>), and an Amazon SNS topic ARN (<code>NotificationChannel</code>). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you want to detect by using the <code>ConnectedHome</code> option in settings, and selecting one of the following: <code>PERSON</code>, <code>PET</code>, <code>PACKAGE</code>, <code>ALL</code> You can also specify where in the frame you want Amazon Rekognition to monitor with <code>RegionsOfInterest</code>. When you run the <a>StartStreamProcessor</a> operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.</p> </li> </ul> <p> Use <code>Name</code> to assign an identifier for the stream processor. You use <code>Name</code> to manage the stream processor. For example, you can start processing the source video by calling <a>StartStreamProcessor</a> with the <code>Name</code> field. </p> <p>This operation requires permissions to perform the <code>rekognition:CreateStreamProcessor</code> action. If you want to tag your stream processor, you also require permission to perform the <code>rekognition:TagResource</code> operation.</p>

    Args:
        input: <p>Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is <code>StreamProcessorInput</code>. This is required for both face search and label detection stream processors.</p>
        output: <p>Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is <code>StreamProcessorOutput</code>. This must be a <a>S3Destination</a> of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.</p>
        name: <p>An identifier you assign to the stream processor. You can use <code>Name</code> to manage the stream processor. For example, you can get the current status of the stream processor by calling <a>DescribeStreamProcessor</a>. <code>Name</code> is idempotent. This is required for both face search and label detection stream processors. </p>
        settings: <p>Input parameters used in a streaming video analyzed by a stream processor. You can use <code>FaceSearch</code> to recognize faces in a streaming video, or you can use <code>ConnectedHome</code> to detect labels.</p>
        role_arn: <p>The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. The IAM role provides Rekognition read permissions for a Kinesis stream. It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.</p>
        tags: <p> A set of tags (key-value pairs) that you want to attach to the stream processor. </p>
        kms_key_id: <p> The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected. </p> <p> </p>
        regions_of_interest: <p> Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest, and each region has either a polygon or a bounding box. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor. </p>
        data_sharing_preference: <p> Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis. Note that if you opt out at the account level this setting is ignored on individual streams. </p>

    Raises:
        capo_rekognition.errors.access_denied_exception.AccessDeniedException: <p>You are not authorized to perform the action.</p>
        capo_rekognition.errors.internal_server_error.InternalServerError: <p>Amazon Rekognition experienced a service issue. Try your call again.</p>
        capo_rekognition.errors.invalid_parameter_exception.InvalidParameterException: <p>Input parameter violated a constraint. Validate your parameter before calling the API operation again.</p>
        capo_rekognition.errors.limit_exceeded_exception.LimitExceededException: <p>An Amazon Rekognition service limit was exceeded. For example, if you start too many jobs concurrently, subsequent calls to start operations (ex: <code>StartLabelDetection</code>) will raise a <code>LimitExceededException</code> exception (HTTP status code: 400) until the number of concurrently running jobs is below the Amazon Rekognition service limit. </p>
        capo_rekognition.errors.provisioned_throughput_exceeded_exception.ProvisionedThroughputExceededException: <p>The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.</p>
        capo_rekognition.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is already being used.</p>
        capo_rekognition.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p></p> <p>The size of the collection exceeds the allowed limit. For more information, see Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide. </p>
        capo_rekognition.errors.throttling_exception.ThrottlingException: <p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>
        capo_rekognition.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.create_stream_processor_request.CreateStreamProcessorRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.create_stream_processor_response.CreateStreamProcessorResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.create_stream_processor

        output, http_response = (
            capo_rekognition._operations.rekognition_service.create_stream_processor.create_stream_processor(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateStreamProcessor")
    )
    input_: capo_rekognition.types.create_stream_processor_request.CreateStreamProcessorRequest = {
        "input": input,
        "output": output,
        "name": name,
        "settings": settings,
        "role_arn": role_arn,
    }
    if tags is not None:
        input_["tags"] = tags
    if notification_channel is not None:
        input_["notification_channel"] = notification_channel
    if kms_key_id is not None:
        input_["kms_key_id"] = kms_key_id
    if regions_of_interest is not None:
        input_["regions_of_interest"] = regions_of_interest
    if data_sharing_preference is not None:
        input_["data_sharing_preference"] = data_sharing_preference

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output